@report{bouland_understanding_2020, title = {Understanding functional consequences of type 2 diabetes risk loci using the universal data integration and visualization R package {CONQUER}}, url = {http://biorxiv.org/lookup/doi/10.1101/2020.03.27.011627}, abstract = {{ABSTRACT} Background Numerous large genome-wide association studies ({GWASs}) have been performed to understand the genetic factors of numerous traits, including type 2 diabetes. Many identified risk loci are located in non-coding and intergenic regions, which complicates the understanding how genes and their downstream pathways are influenced. An integrative data approach is required to understand the mechanism and consequences of identified risk loci. Results Here, we developed the R-package {CONQUER}. Data for {SNPs} of interest (build {GRCh}38/hg38) were acquired from static- and dynamic repositories, such as, {GTExPortal}, Epigenomics Project, 4D genome database and genome browsers such as {ENSEMBL}. {CONQUER} modularizes {SNPs} based on the underlying co-expression data and associates them with biological pathways in specific tissues. {CONQUER} was used to analyze 403 previously identified type 2 diabetes risk loci. In all tissues, the majority of {SNPs} (mean = 13.50, {SD} = 11.70) were linked to metabolism. A tissue-shared effect was found for four type 2 diabetes-associated {SNPs} (rs601945, rs1061810, rs13737, rs4932265) that were associated with differential expression of {HLA}-{DQA}2, {HSD}17B12, {MAN}2C1 and {AP}3S2 respectively. Seven {SNPs} were identified that influenced the expression of seven ribosomal proteins in multiple tissues. Finally, one {SNP} (rs601945) was found to influence multiple {HLA} genes in all twelve tissues investigated. Conclusion We present an universal R-package that aggregates and visualizes data in order to better understand functional consequences of {GWAS} loci. Using {CONQUER}, we showed that type 2 diabetes risk loci have many tissue-shared effects on multiple pathways including metabolism, the ribosome and {HLA} pathway.}, institution = {Genomics}, type = {preprint}, author = {Bouland, Gerard A and Beulens, Joline {WJ} and Nap, Joey and van der Slik, Arno R and Zaldumbide, Arnaud and Hart, Leen M’t and Slieker, Roderick C}, urldate = {2020-11-20}, date = {2020-03-29}, langid = {english}, doi = {10.1101/2020.03.27.011627}, keywords = {{WP}3}, }